Structured replacement policies for components with complex degradation processes and dedicated sensors

Failure of many engineering systems usually results from a gradual and irreversible accumulation of damage, a degradation process. Most degradation processes can be monitored using sensor technology. The resulting degradation signals are usually correlated with the degradation process. A system is considered to have failed once its degradation signal reaches a prespecified failure threshold. This paper considers a replacement problem for components whose degradation process can be monitored using dedicated sensors. First, we present a stochastic degradation modeling framework that characterizes, in real time, the path of a component's degradation signal. These signals are used to predict the evolution of the component's degradation state. Next, we formulate a single-unit replacement problem as a Markov decision process and utilize the real-time signal observations to determine a replacement policy. We focus on exponentially increasing degradation signals and show that the optimal replacement policy for this class of problems is a monotonically nondecreasing control limit policy. Finally, the model is used to determine an optimal replacement policy by utilizing vibration-based degradation signals from a rotating machinery application

Residual life predictions in the absence of prior degradation knowledge

Recent developments in degradation modeling have been targeted towards utilizing degradation-based sensory signals to predict residual life distributions. Typically, these models consist of stochastic parameters that are estimated with the aid of an historical database of degradation signals. In many applications, building a degradation database, where components are run-to-failure, may be very expensive and time consuming, as in the case of generators or jet engines. The degradation modeling framework presented herein addresses this challenge by utilizing failure time data, which are easier to obtain, and readily available (relative to sensor-based degradation signals) from historical maintenance/repair records. Failure time values are first fitted to a Bernstein distribution whose parameters are then used to estimate the prior distributions of the stochastic parameters of an initial degradation model. Once a complete realization of a degradation signal is observed, the assumptions of the initial degradation model are revised and improved for future predictions. This approach is validated using real world vibration-based degradation information from a rotating machinery application

Condition-based maintenance for systems with aging and cumulative damage based on proportional hazards model

This paper develops a condition-based maintenance (CBM) policy for systems subject to aging and cumulative damage. The cumulative damage is modeled by a continuous degradation process. Different from previous studies which assume that the system fails when the degradation level exceeds a specific threshold, this paper argues that the degradation itself does not directly lead to system failure, but increases the failure risk of the system. Proportional hazards model (PHM) is employed to characterize the joint effect of aging and cumulative damage. CBM models are developed for two cases: one assumes that the distribution parameters of the degradation process are known in advance, while the other assumes that the parameters are unknown and need to be estimated during system operation. In the first case, an optimal maintenance policy is obtained by minimizing the long-run cost rate. For the case with unknown parameters, periodic inspection is adopted to monitor the degradation level of the system and update the distribution parameters. A case study of Asphalt Plug Joint in UK bridge system is employed to illustrate the maintenance policy